ABSTRACT. What would be your first thoughts and actions if you were caught in a short-onset major crisis, such as an earthquake? Once the initial threat was over, would you go immediately to a safe area, or would you try, for example, to find your loved ones possibly putting yourself in danger to do so? The occurrence of large-scale disasters is increasing and authorities are constantly being challenged in their ability to respond and manage them effectively. This talk looks as crisis as a complex socio-technical system from both the point of view of crisis managers and victims. Using agent-based modelling and simulation we try to represent realistic human behaviours in crisis situations in order to develop more suitable crisis response actions. Crisis victims are not homogenous in their behaviours but are influenced by a wealth of factors including their previous experience of disasters, knowledge of what is the « right » thing to do, social attachments, social vulnerability, and the physical environmental context, to name a few. Likewise, emergency managers often have to adapt their prescribed plans since the intricacies and conditions surrounding each crisis is different. We describe the challenges in modelling human behaviours and show examples of the computer simulators that we have developed.

ABSTRACT. While it is known that diffusive coupling of elements in an oscillatory medium can give rise to a variety of spatiotemporal patterns, the effect of advection on such patterns remains an open question. It is of particular relevance in the context of predator-prey population dynamics occurring in the ocean (e.g., Phytoplankton Zooplankton population dynamics that can be written in terms of reaction-diffusion equation). In this paper, the reaction-advection-diffusion (RAD) framework is used to investigate the effect of advective interactions on the spatiotemporal patterns that arise in an oscillatory medium. Using a generic model of an activator-inhibitor system, it is first shown that the emergent dynamics observed in a continuous medium exhibit qualitatively similar trends to that of an equivalent system of discrete diffusively coupled oscillators in both one- and two-dimensional domains. In particular, a transition from oscillating to non-oscillating stationary patterns is observed as the diffusion coefficient is increased. The effect of stirring on these patterns is investigated by introducing a chaotic flow into the medium. While high stirring invariably results in spatial homogenization, for a specific regime we observe that certain aspects of the spatiotemporal dynamics are preserved.

ABSTRACT. Global population dynamics are changing due to the complex interaction of technological innovations, through the 20th century, and mathematical population models must adapt to reflect the changing nature of modern networks. We present a novel, non-linear Markov chain model of population growth with relevance to naturally monogamous, pair-bonding societies and show that the results of numerical solutions for such a system are non-trivial, resulting in a population plateau. We argue that this plateau behaviour could explain the popular observation of Dunbar’s number, as a limit of social linkage abundance, in human societies. Finally, we present a Markovian network model, which extends the earlier Markov chain model’s relevance to modern societies, where constrained sub-networks can give rise to exponentially growing societies. Certain parameter sets shed light on the evolutionary and sociological consequences of human behaviour. These quantitative observations, around the phenomena of individual social networking and the meta-phenomenon of societal population growth, may allow more accurate modelling and prediction of the consequences of technological innovations for population growth and social organisation.

ABSTRACT. The material flow analysis makes it possible to obtain "reconciled" use-supply tables from "raw" use-supply tables, i.e. incomplete, uncertain and inconsistent. It can be used to model existing socio-technical systems. However, this framework can also be used to study systems that are modelled but do not exist and whose plausibility we want to question, which we call socio-technical alternatives. To model these alternatives, we will build modules, i.e. functions that output the data that is then reconciled. The input variables of these functions, called indicators, and their combination to obtain data, give a quantitative description of the alternative. This quantitative description can be checked and thus the plausibility of an alternative can be judged with the reconciliation of the data.

ABSTRACT. Spatial complex networks can be observed in many natural and artificial systems. An important aspect to investigate is the way that they form and evolve. In this paper we present a geometric graph generator, where the morphogenesis is driven by a reaction-diffusion system which represents the evolving environment of the network. Dynamic and spatial patterns constraint the network through a dynamic vector field.

ABSTRACT. This work focuses on dynamic graphs generation. Attention is paid on a particular endogenous process enabling a dynamic graph, starting from an initial graph, to evolve such that some key features (node degree, graph order, etc.) remain bounded while the underlying graph is evolving. We call these graphs self-sustained dynamic graphs. An analytical generative model for such graphs is proposed. This model mirrors Conway's Game of life cellular automaton, with a ruleset controlling the creation and destruction of nodes and edges.

ABSTRACT. This is an extended abstract of our article entitled Higher-Order Spectral Clustering for Geometric Graphs, and which is devoted to clustering geometric graphs.

While the standard spectral clustering is often not effective for geometric graphs, we present an effective generalization, which we call higher-order spectral clustering.

It resembles in concept the classical spectral clustering method but uses for partitioning the eigenvector associated with a higher-order eigenvalue. We establish the weak consistency of this algorithm for a wide class of geometric graphs which we call Soft Geometric Block Model. A small adjustment of the algorithm provides strong consistency. We also show that our method is effective in numerical experiments even for graphs of modest size.

Reference of the full article: Konstantin Avrachenkov, Andrei Bobu, Maximilien Dreveton. Higher-Order Spectral Clustering for Geometric Graphs. \textit{Journal of Fourier Analysis and Applications}, 27(2), 1-29, 2021.

ABSTRACT. We illustrate a simple connection between the cycles in a directed graph and eigenvalues its the adjacency matrix. Then we use this connection to derive properties of the eigenvalues of two families of random directed signed graphs: graphs with short cyclic motifs and circulant graphs. In doing so we present two beautiful eigenvalue distributions that have practical relevance for Reservoir Computing. This abstract summarizes an existing manuscript (Aceituno. "Eigenvalues of random graphs with cycles." arXiv preprint 1804.04978 (2018).

ABSTRACT. The Covid19 pandemic has awakened recurrent debates about the future of cities. Some speculate on a gradual abandonment of large metropolises and a return of populations to the countryside, or to small or medium-sized towns. Similar predictions invoking a trend of « counter-urbanization » had been made in the mid-1970s but did not materialize. Before risking predictive hypotheses, a thorough analysis of the dynamics of cities is necessary. We observe how cities, which are organized in systems of cities, maintain their size relationships over time. The main explanation for this persistence lies in the interaction pattern between cities and urban stakeholders and its slow path-dependent evolution. Dynamic models of complex systems can simulate this development. They provide elements to explain the cognitive dissonance between individual and collective ways of inhabiting the world. They also make it possible to envisage reasoned territorial planning for a successful ecological transition and to reduce inequalities.

ABSTRACT. This paper highlights the two-ways relationship between data and entities in the context of tourism management. Indeed, the tourists left various traces of their planning and trip on social media and networks. Moreover, the studying of those data to feed tourism marketing also affect the future data left by tourists. Understanding the intrinsic and extrinsic relationship between tourism entities should significantly improve the knowledge in this research field.

ABSTRACT. The core-periphery structure of a network partitions the nodes into two groups. The nodes belonging to the core are densely interconnected, and the peripheral nodes are sparsely linked. Numerous algorithms have been proposed so far to uncover this mesoscale structure commonly encountered in many real-world networks. This paper introduces a new solution to this issue to explore the world air transportation network's core-periphery. The network is split into local and global components based on its community structure. The core-periphery structure of the various components is then extracted. Comparisons with the entire original network's core-periphery structure show that this strategy is much more informative about the relative influence of international and regional airports.

ABSTRACT. Urban systems are intrinsically complex, involving different dimensions and scales, and consequently various approaches and scientific disciplines. In that context, urban simulation models have been coined as essential for the construction of evidence-based and integrated urban sciences. This review and position paper synthesises previous work focused on coupling and integrating urban models on the one hand, and exploring and validating such simulation models on the other hand. These research directions are complementary basis for a research program towards the development of integrated urban theories, with some application perspectives to sustainable territorial planning.

ABSTRACT. Nowadays, modern Earth Observation (EO) systems continuously generate huge amounts of data. A notable example is the Sentinel-2 EO mission, developed by the European Space Agency (ESA) as part of the Copernicus Programme, which supplies images from the whole planet at high spatial resolution (up to 10m) with unprecedented revisit time (every 5 days at the equator). Thanks to the high revisiting period, such data can be profitably organized in Satellite Image Time Series (SITS), that represent a relevant source of information, expected to support a plethora of land monitoring tasks, such as fire mapping, forest and agricultural monitoring and climate change analysis. Moreover, since Sentinel-2 SITS are publicly available, they represent an attractive source for research purposes. Recently, we showed how SITS can be profitably represented using complex network models, by proposing an unsupervised method to extract a Multilayer Network from them, which integrates spectral, spatial and contextual information. The availability of such a multilayer network model to represent SITS paves the way for the definition of unsupervised remote sensing methods inspired from advanced network analysis techniques, which may represent an extremely valid support to drive data hungry supervised techniques, by limiting their needs, in a context where collecting data with corresponding ground truth labels is particularly costly in terms of days of field work per study site. In this work, we focus on an agricultural landscape stratification task, demonstrating how it can be addressed in an unsupervised fashion by resorting to multilayer community detection algorithms. The goal is to automatically provide a stratification of the landscape into different sub-systems, focusing in particular on the identification of areas dominated by cropping practices. We will provide experiments at different scale (i.e., local to national level), and on different landscapes (i.e., characterized by different vegetation and seasonal cycles), in order to investigate to what extent multilayer communities can be used to retrieve such sub-systems.

ABSTRACT. A humanitarian crisis relief system involves many entities of various types in interaction. To study such a system, the complex network framework is a good candidate. Some authors proposed mono-layered networks to model and analyze a single aspect of the system. To oer a broader view, we propose a multilayer model. Hence, the multifaced aspect of the system appears in a unied manner.

ABSTRACT. One of the major current challenges in the field of security and safety of populations is to progress in the understanding and ability to anticipate their behaviors and their spatial trajectories, when faced to threats or disasters. Our purpose is to mathematically model and analyse how the territory properties concerning evacuation, flight or accessibility to refuge areas can force the spatio-temporal dynamics of human behaviors.

ABSTRACT. Road network construction results from a subtle balance between geographical coverage and rapid access to the strategic points of space. The understanding of the road network structure is fundamental to evaluate and improve territorial accessibility. In this ongoing work, a morphological analysis of road patterns over time is developed on three cities (Besançon, Dijon, Pontarlier), over three historical periods (the 18th, 19th and 20th centuries). This study is based on geo-historical data provided by historical maps. Those maps allow the digitization of historical road networks. From those networks, a graph is built, allowing the computation of indicators based on their topological and geometrical properties. Thus, it is possible to compare their characteristics and highlight valuable information through space and time. In a prospective vision, this work aims to identify mechanisms leading road network evolution. Studying road network morphogenesis to detect indicators stability or variation over time, and to identify similar behaviors, despite geographic and cultural distances, is of major support for better understanding their impact on access and mobility.

ABSTRACT. Graph embedding aims to learn a representation of graphs' nodes in a latent low-dimensional space. The purpose is to encode the graph’s structural information. While the majority of real-world networks is dynamic, literature generally focuses on static networks and overlooks evolution patterns. In a previous article entitled "TemporalNode2vec: Temporal Node Embedding in Temporal Networks", we introduced a dynamic graph embedding method that learns continuous time-aware vertex representations. In this paper, we adapt TemporalNode2vec to tackle especially the node classification related tasks. Overall, we prove that task-specific embedding improves data efficiency significantly comparing to task-agnostic embedding.

ABSTRACT. Stream graphs are a very useful mode of representation for temporal network data, whose richness offers a wide range of possible approaches. The various methods aimed at generalising the classical approaches applied to static networks are constantly being improved. In this paper, we describe a framework that extend to stream graphs iterative weighted-rich-clubs characterisation for static networks proposed in [1]. The general principle is that we no longer consider the membership of a node to one of the weighted-rich-clubs for the whole time period, but each node is associated with a temporal profile which is the concatenation of the successive memberships of the node to the weighted-rich-clubs that appear, disappear and change all along the period. A clustering of these profiles gives the possibility to establish a reduced list of typical temporal profiles and so a more in-depth understanding of the temporal structure of the network. This approach is tested on real world data produced by recording the interactions between different students within their respective schools. [1] M. Djellabi, B. Jouve, and F. Amblard. Dense and sparse vertex connectivity in networks. Journal of Complex Networks, 8(3), 2020.

ABSTRACT. The effects of algorithmic recommendation in online systems is the subject of growing interest and, also, of sometimes conflicting results, depending on whether it is said to contribute to expand or to restrain the horizon and serendipity of users. We will recontextualize this debate by distinguishing different places of action of algorithmic devices on platforms: upstream (principles), in situ (suggestions) and downstream (uses). We further illustrate each case with recent results. Upstream, we show that the design of these algorithms is similar to a largely self-piloted evolutionary dynamics. In situ, we describe topological and likely navigational biases induced by content suggestion on YouTube, relying in particular on an extensive collection of ad hoc data. Downstream, we exhibit a variety of attitudes toward recommendation using data from the online music listening platform Deezer, showing in particular how users’ preferences affect the influence of recommendation, rather than the other way around.

ABSTRACT. There are several opinion dynamics models where extremism is defined as part of their characteristics. However, the way extremism is implemented in each model does not correspond to equivalent definitions. While some models focus on one aspect of the problem, others focus on different characteristics. I will present a model where both characteristics can be defined so that discussing the meaning of extremism can be done on more solid mathematical grounds.

ABSTRACT. Interest in handling the contemporary sustainability and social ecological system issues (SES) can manifest itself in explorations of sustainability oriented transformation of European Regions. Such cases can be explored in complex systems approaches – based on quantified system model activities as well as based on socio cultural complexity related narratives (e.g. scenario writing) – and combination of both methods. The two authors of this Extended Abstract has finalized the Swedish part of a EU project 2012-16 under the name COMPLEX (together with coworkers). The authors led the two university groups that jointly provided the contributions of the EUprojects Work Group number 4 - i.e. the Swedish part. As its thematic target the regional development within a sustainability framework for the Stockholm – Lake Mälar region was explored. This was done both with the development of sectoral models as well as together with stakeholder engagement groups and game theoretical approaches involving decision makers of various kinds. During the project - which has been reported in the science literature (see selected references below) –a number of complexity oriented approaches were used and combined in order to build up a connected systems picture of the current situation for the region, the key issues of concern as well as policy recommendations at regional and national levels based on the investigations. In this Extended Abstract we draw on the finished research in the EU project in order to provide a research experience based contribution about complexity oriented systems issues in our chosen area and with thematic regional development connotations. We provide our experiences with regard to our assembly of complex model approaches as well as other more qualitative features e.g. the relationship with stakeholders and decision makers. Our contribution here is thus to go through a set of complex partial research contributions as well as providing an understanding how we have combined these results to provide a broader more holistic background for the regional development of the chosen geographical test domain. In addition to the exemplification of the parts of complex systems modelling we will also contribute by upgrading our policy advice section a few years after the completion of the EU project in 2016. In order to provide an understanding how we consider the research pieces to exemplify complex systems issues we make note below of some characteristics: a) The overriding socio economic and ecological development of a major Swedish region, with a core in the capital of Sweden and geographically stretching from the Baltic sea (in the east) westwards some 600 kilometers into the lake Mälar area. This agglomerate of both urban and rural areas encompassing around 3 million people and a connected one third of the Swedish economy is in itself a complex system and has complex features in its development in terms of past and future emergence. In order to consider the major chosen challenge i.e. the path to non-fossil society in this region in accordance with the relevant SDG:s the complex nature of the total system has to be considered. This holds true not only for the energy/industry related issues of the fossil character of the region, but every part of it that has an influence on the possibility to build a fossil free (i.e. net zero) future – normally in the Swedish context time wise set around the years 2045/2050 in a contemporary policy making framing. b) We here have a very clear example of a multi layered governance structure: from individuals, municipalities, urban centers (sometimes referred to as “cities” – big and small), sub regions and the entire region. In addition they are all set in the context of the Swedish nation and its Nordic and EU involvement – as well as in the pattern of international trade. c) We have also a multi faceted sectoral interplay between different sectors as energy, transport, food, water, tourism etc. d) The governance structure that match the different geographical scalar entities have different characteristics in terms of responsibilities, economics - including taxation - framings as well as general rules and regulations. Here the interplay also at the regional level between the regional/municipality oriented authorities and the regional representatives of the state has intricate and dynamically changing features of great impact on the change characteristics of the entire region) e) Different stakeholder types from individuals and their local associations and NGO´s to small industry and larger consortia in the business community – including their strategy grouping consortia (mostly of economic kinds) are part of the entire social complex system with its historical development and future options and possibilities. f) with regard to individuals the decision making on day to day matters (as if a certain individual should consider taking the car a particular morning when it is raining or not) provides entries for reflections about complex behavior based in neuro network features of individuals g) Also collective decision making bodies can be interpreted as network entities put under different types of stress. This has been studied in computer based games under stress conditions. This holds true also about issues in the decision making sphere dealing with long term decisions about social change and transformation i.e. sustainability considerations. As an assembly of what has been said above we consider our research territory to be full of complex system core issues: multi-scalar phenomena, non linear resilience systems oriented challenges over time, human-nature behavioral relations and the special domain of information handling, including digitalization and its connection to sustainability performance Our contribution with our Extended Abstract is to demonstrate a multi faceted complexity prone situation of regional size and character in Europe.

Associated reports availabilities i.e. material with author responsibilities by Liljenström and Svedin. Specifically the following two information packages are available: 1. Summary Report from the Swedish part of the EU-COMPLEX project (Liljenström and Svedin) 2. A book chapter on “Agglomeration” aspects of the studied area published by IntechOpen 2018 (Authors: Svedin and Liljenström)

ABSTRACT. The behavior of complex systems is intrinsically difficult to model and to predict. Agri-food chains can be considered as such. The problem considered in this paper comes from the commitment to anticipate the impacts of the implementation of innovations in such highly complex agri-food systems. The paper focuses on comparative methodological issues, when seeking to anticipate the possible evolutions of the system. Indeed, this article proposes adaptations in the classic “scenario method” because of constraints of remote working generalized during the pandemic, and discusses possible biases induced by these adaptations in the results obtained. The methodological and organizational differences are described, and show that the remote constraints do not prevent from delivering some “key variables” of the system. The adapted method is illustrated in a case study in the pork supply chain. Nevertheless, the face-to-face collaborative sessions generating a consensus among players in the classic method can not be replaced in the remote context. As a consequence, it is likely that some key variables that would have been selected thanks to consensus in the classical method are let aside in the adapted method, because the number of prospects quoting them spontaneously in individual interviews is not large enough. The following consequence is that the scenarios that would have been generated thanks to the various values of these likely key variables, are not taken into account. It is thus likely that less scenarios are depicted by the adapted method than by the classic one.

ABSTRACT. This paper presents a new framework to study social complexity formation, positing causal mechanisms of decision-making in shaping flows of energy, resources, and information as an important driving factor of social complexity. We use the perspective of complex systems thinking to provide a bottom-up approach to social complexity as an emergent property in human societies. The framework is applied to a case study of social complexity trajectories in southwest Anatolia (modern-day Turkey), spanning almost three millennia from Bronze Age to Hellenistic times. The present study will not only strongly enhance our knowledge of social complexity formation in the past, but also elucidate how an improved understanding of long-term trajectories of social complexity can inform trade-offs in decision-making and pathways of development in the present.

ABSTRACT. The question of why we age is a fundamental one. It is about who we are, and it also might have critical practical aspects as we try to find ways to age slower. Or to not age at all. Different reasons point at distinct strategies for the research of anti-ageing drugs. While the main reason why biological systems work as they do is evolution, for quite a while, it was believed that aging required another explanation. Aging seems to harm individuals so much that even if it has group benefits, those benefits were unlikely to be enough. That has led many scientists to propose non-evolutionary explanations as to why we age. But those theories seem to fail at explaining all the data on how species age. Here, I will show that the insistence of finding the one idea that explains it all might be at the root of the difficulty of getting a full picture. By exploring an evolutionary model of aging where locality and temporal changes are fundamental aspects of the problem, I will show that environmental change causes the barrier for group advantages to become much weaker. That weakening might help small group advantages to add up to the point they could make an adaptive difference. To answer why we age, we might have to abandon asking which models are correct. The full answer might come from considering how much each hypothesis behind each existing model, evolutionary and non-evolutionary ones, contributes to the real world's solution.